Most present day digital computers are controlled by a program to process data according to a comprehensive set of rules incorporated into the program. However, in many situations, such a rule-based system does not work well. The rules for image recognition, an example of a much larger problem class, are complex and not really well known in strictly logical terms. Furthermore, image recognition does not inherently require the precision available in a digital computer. Recognition of a single image requires only a single-bit yes/no answer. A multi-category classification requires a limited range of decisions, for example, 10 possible answers for an Arabic-number reader.
To overcome the above limitations of a digital computer, many research groups have suggested learning and recognition systems based on associative memories, which are reminiscent of human memory and its learning process. In broad terms, these systems are taught by presenting to them sample input patterns, e.g., images, while simultaneously forcing their output to the correct answer. By various means, the internal connections of the learning system are readjusted during the simultaneous setting of its input and output. After completion of learning, the system, when presented with a learned input pattern, will produce the learned output. The system becomes advantageous when it can recognize a pattern that differs somewhat from the learned pattern.
Apart from computer simulations, most learning machines that have actually been fabricated are electronic in nature and are often referred to as neural networks. However, it is difficult to implement neural networks of a practical size because of the complexity of the interconnections within the system.
Several groups have suggested optical learning machines based on holographic memories. Psaltis et al. provide a review of the theory of holographic learning machines in "Adaptive optical networks using photorefractive crystals," Applied Optics, volume 27, 1988, pages 1752-1759. However, they described an implementation that could learn only a single pattern and could be extended to multiple category classification only with much difficulty.
Hong et al. have proposed a modification of the Psaltis machine in "Trainable Optical Network for Pattern Recognition," Optical Computing: 1989 Technical Digest Series, Volume 9, Postconference Edition, Salt Lake City, Utah, 1989, pages 307-310, and in "Optical pattern classifier with Perceptron learning," Applied Optics, volume 29, 1990, pages 3019-3025. They proposed a form of bipolar learning, that is, positive feedback for a correct output and negative feedback for incorrect outputs, by shutters selectively blocking two beams producing anti-phase beams. The proposals of both Psaltis et al. and Hong et al. amount to incomplete implementations of the perceptron algorithm in which the interconnection matrix is bipolar, but they lose the polarity of the output of the interconnection matrix, which is important in making a decision. Furthermore, although they suggested ways to achieve multicategory classification using spatial multiplexing of the reference beam, their design appears unwieldy. Hong et al. have described the actual operation of this learning machine for single-category classification in a later article, "Adaptive Optical Pattern Classifier," Conference Record of 1990 International Topical Meeting on Optical Computing, Kobe, Japan, 1990, pages 266-267.
Another optical learning machine has been disclosed by Yoshinaga et al. in "Experimental learning in an optical perceptronlike neural network," Optics Letters, volume 14, 1989, pages 716-718. Only a single alignment of image and reference beams and only a single optical detector were used. Therefore, their system was only capable of single-category classification. Furthermore, the interconnection matrix and the output signal were necessarily unipolar and the perceptron algorithm was only partially implemented.
The optical learning machines described above must be viewed as experimental designs. Many improvements and fundamentally different designs are needed before optical learning machines can be commercialized. Multi-category classification is needed for most applications. All the prior art apparatus are assumed to all have been implemented on an optical table of large dimension using discrete optical devices with precise alignment required. Commercial devices need to be small, easily aligned, and relatively rugged.
Prior-art holographic learning machines had a further problem arising from their relatively low learning efficiency. Typical holographic media used in learning machines use photorefractive crystals which are not permanently recorded but decay over time to the unrecorded state. To compensate, the prior-art machines used high laser recording intensities which recorded the memory crystal close to its saturation level. Although the crystal was recorded fairly quickly to within a few percent of its saturation, a very large laser intensity was required for saturation. Conversely, the saturated recording quickly decayed to a few percent of its saturated value, after which it more slowly decayed. As a result, the crystal was decaying nearly as fast as it could be recorded, thus limiting the number of classes with which it could be taught.